Abstract: This paper presents a model in which financial innovations explain three widely discussed stylized facts regarding trends in economic volatility over the past two decades. Aggregate volatility of real variables such as output has fallen. In particular, the covariance between firm and industry activities has declined, and so has employment volatility for the majority of firms. In contrast, the volatility of quantities of financial variables has increased at both the firm and aggregate level.

The model links these outcomes to a single hypothesized cause: advances in financial technology brought about by a declining cost of information processing. As a result, the marginal cost of external funds has likely declined, reducing the need for firms to smooth cash flows. Firms, trading off cash-flow vs. production smoothing, therefore have more incentive to smooth production. This explains why financial volatility may go up as real volatility goes down. Moreover, financial innovations have likely also altered the composition of volatility toward a greater share of idiosyncratic risk, by facilitating diversification and thus lowering the premium demanded on idiosyncratic risk.

At the margin, the cost advantage to projects with idiosyncratic returns reduces the covariance of financial as well as real activities across firms. Since variance and covariance of real quantities trend in the same direction, real aggregate volatility declines. But the net effect on financial variables is ambiguous and so can yield greater aggregate volatility. The paper then presents evidence that the share of idiosyncratic risk has risen in bank portfolios, indicating that the same has occurred for individual borrowers as well.

At night in the suburbs of San Francisco, some of us awake as the hills echo and re-echo with the howls of the coyotes that have fed well on Glenn Rudebusch's chickens. We then lie awake, worrying. We worry why the Great Moderation in the U.S. business cycle on the real side that we have seen since the mid-1980s has not carried a big reduction in financial-side variability with it. We toss and turn, worrying that the real-side volatility decline has been part good transitory luck and part statistical illusion, all because people in financial markets putting their money where their mouths were do not project the continuation of the Great Moderation into the future.

Christina Wang's paper lets us sleep more easily, even if the coyotes continue to prey upon the chickens of Federal Reserve Bank Vice Presidents. It teaches us an important and valuable lesson: a financial system that is doing a better job will be highly likely to have both higher financial and lower real volatility.

When a firm goes bankrupt and defaults on its debt, it may be because it has had bad luck, it may be because it was badly managed, or it may be because it suffered from moral hazard--took account of the fact that in the lower tail the losses are eaten not by the firm but by the bank that loaned it the money. Banks that have a hard time distinguishing between these possibilities will be averse to lending--charge a high interest rate premium on loans--to firms seen as having a high degree of undifferentiated idiosyncratic risk. Improvements in data collection and analysis that allow firms to differentiate will cause banks to fear undifferentiated firm-level idiosyncratic risk less, and charge lower interest rate premiums for such lending. Other things being equal, firms will smooth production more, and smooth cash-borrowing requirements less, seeking to squeeze out more productive efficiencies by taking on more financial risk. To the extent that improvements in data collection and analysis reduce banks' fixed costs of monitoring loans, other things being equal banks will do more to diversify away firm-level idiosyncratic risk.

When a bank goes bankrupt and defaults on its debt, it may be because it has had bad luck, it may be because it was badly managed, or it may be because it suffered from moral hazard--took account of the fact that in the lower tail the losses are eaten not by the banks' shareholders but by those who hold or guarantee its liabilities. Improvements in data collection and analysis by those to whom banks owe their liabilities will allow them to better classify banks, and so the cost to banks of portfolios with bank-level idiosyncratic risk will fall. Other things being equal, banks will be willing to take on more bank-level idiosyncratic risk.

Of course this function that Christina Wang identifies is the primary job--one of the primary jobs--of financial markets: to diversify away idiosyncratic risk, as was ably explicated by that notable predecessor of Lintner and Markowitz, William Shakespeare. As Shakespeare writes, Antonio, the Merchant of Venice, does not fear that the lower tail of his portfolio return distribution extends far enough down to the state in which his heart is cut out with a knife. Antonio he has a properly-diversified portfolio. The banker lending him the money uses the highest information technology of that day: wandering down to Venice's Grand Canal, loitering on the High Bridge, and gossiping. The banker concludes that Antonio has:

an argosy bound to Tripolis, another to the Indies; I understand moreover, upon the Rialto, he hath a third at Mexico, a fourth for England, and other ventures...

Here the analogy breaks down. Negative transitory systematic news does indeed provoke a crisis in Antonio's affairs, but he is rescued not by a competent, technocratic lender of last resort but by his bride disguised as a teenage judge.

Christina Wang hopes that starting sometime in the mid-1980s we took a jump toward the ideal financial world in which one of CAPM's cousins holds, in which idiosyncratic risk is not priced because it is properly diversified away, and in which as a result the real economy can grab for all the production-smoothing efficiency benefits without worrying about firm- or bank-level costs of default or illiquidity. This shift could drive a reduction in real-side volatility coupled with an increase or no change in financial-side volatility.

She has a nice theoretical costly-state-verification model of the effects of improved data collection and analysis technologies. She has a very interesting theoretical Dixit-Stiglitz-based three-period model of the joint determination of real and financial volatility. The key insight is a very good one: that production-smoothing has not just manufacturing-side and labor-side efficiency benefits but financial-side efficiency costs: only if banks are confident in their ability to monitor firms and large depositors confident in their ability to monitor banks will firms be able to easily and cheaply borrow the money they need in recession to enable a production-smoothing corporate strategy. The fact that times of recession are times when a firm's free cash is likely to be uniquely valuable and not to be best invested in building up inventories is a potentially powerful explanation of why we have, historically, seen the reverse of production-smoothing in the American economy. She has interesting empirical results that suggest that banks and firms have reacted to a likely information-driven fall in the cost of idiosyncratic financial risk to take on more of it. The theory is sound and convincing. The micro empirics are interesting and suggestive.

But how much can this channel add up to on the macro level? How, exactly, does ICT help bankers? Working for the original J.P. Morgan, Charlie Coster was on the boards of 88 railroads at the turn of the last century and died of overwork--Morgan is reputed to have recruited Coster's successor while they were together carrying Coster's coffin to its grave. What would today's ICT have done to increase Coster's contribution to Morgan's bottom line, exactly?

And how much of the Great Moderation in real-side economic volatility can this channel account for? Recall the size of the Great Moderation: a 40% fall in the standard deviation of the cyclical component of GDP, more or less the same however you choose to measure it. A fall in spite of the fact that technology and cost shocks have in all likelihood been quantitatively greater in the past ten years than in any other post-WWII decade save possibly the 1970s.

As Christina Wang says, her paper as written can't do the job. It can only do about a third of the job--although Doug Elmendorf said half last hour. The model as extended quite possibly could.

In this literature, the game that is being hunted is the positive correlation between production and inventory investment that we saw in the past. In a standard production-smoothing model inventory investment should be relatively high when production is relatively low, and sales are very low. Instead--back before 1985--inventory investment was high when production was high. This shift could be possibly traced to Christina Wang's mechanisms. But it can account, in my back-of-the-envelope guess, for not a 40% but a 15% decline in the standard deviation of the cyclical component, whatever that is.

The big game for this model--as Chistina Wang says in her conclusion--will, I think, come from applications of models like this to the household sector. It's not just firms that have benefitted from the application of information technology to credit screening. I have gotten three offers of VISA cards and two offers of what were described as "guaranteed low interest" home-equity loans so far this week. Plus the people behind the counter at my most local Starbucks have started asking me if I'm interested in a no-annual-fee Starbucks VISA that will come with $25 of free caffeinated drinks. I don't know whether they are doing this to everybody or whether there is something special in my file. The smoothing-out of household durables purchases will, I think, be an important part of the Great Moderation when we finally nail it down. And I think that's where the high returns from Christina Wang's model will come.

Last, the smoothing out of residential construction--if it indeed stays smoothed-out--may well turn out to be the heart of the matter. One branch of the conventional wisdom is that the smoothing-out of residential construction is a result of good luck that is about to end: that America's banks have been offered too much rice wine by the People's Bank of China, and have responded by lending like drunken bankers: $600,000 zero-down floating-rate loans to single-earner middle-class families buying three-bedroom houses in Vallejo, CA: and we will be sorry.

Christina Wang's paper suggests a second possible explanation. That recent residential investment financed by so-called "non standard" mortgage loans is a result at least in part not of the inebriation of the banking sector but of the ability to more finely calculate risk and return than was possible in the days when your mortgage had to be 30-year-fixed, 20% down, with amortization plus real estate taxes amounting to no more than 33% of last year's household income. That was an inadequate screen. What, really, are the current screens? How good are they? The application of models like this to residential financing may be the real big game here.

Abstract: This paper presents a model in which financial innovations explain three widely discussed stylized facts regarding trends in economic volatility over the past two decades. Aggregate volatility of real variables such as output has fallen. In particular, the covariance between firm and industry activities has declined, and so has employment volatility for the majority of firms. In contrast, the volatility of quantities of financial variables has increased at both the firm and aggregate level.

The model links these outcomes to a single hypothesized cause: advances in financial technology brought about by a declining cost of information processing. As a result, the marginal cost of external funds has likely declined, reducing the need for firms to smooth cash flows. Firms, trading off cash-flow vs. production smoothing, therefore have more incentive to smooth production. This explains why financial volatility may go up as real volatility goes down. Moreover, financial innovations have likely also altered the composition of volatility toward a greater share of idiosyncratic risk, by facilitating diversification and thus lowering the premium demanded on idiosyncratic risk.

At the margin, the cost advantage to projects with idiosyncratic returns reduces the covariance of financial as well as real activities across firms. Since variance and covariance of real quantities trend in the same direction, real aggregate volatility declines. But the net effect on financial variables is ambiguous and so can yield greater aggregate volatility. The paper then presents evidence that the share of idiosyncratic risk has risen in bank portfolios, indicating that the same has occurred for individual borrowers as well.

At night in the suburbs of San Francisco, some of us awake as the hills echo and re-echo with the howls of the coyotes that have fed well on Glenn Rudebusch's chickens. We then lie awake, worrying. We worry why the Great Moderation in the U.S. business cycle on the real side that we have seen since the mid-1980s has not carried a big reduction in financial-side variability with it. We toss and turn, worrying that the real-side volatility decline has been part good transitory luck and part statistical illusion, all because people in financial markets putting their money where their mouths were do not project the continuation of the Great Moderation into the future.

Christina Wang's paper lets us sleep more easily, even if the coyotes continue to prey upon the chickens of Federal Reserve Bank Vice Presidents. It teaches us an important and valuable lesson: a financial system that is doing a better job will be highly likely to have both higher financial and lower real volatility.

When a firm goes bankrupt and defaults on its debt, it may be because it has had bad luck, it may be because it was badly managed, or it may be because it suffered from moral hazard--took account of the fact that in the lower tail the losses are eaten not by the firm but by the bank that loaned it the money. Banks that have a hard time distinguishing between these possibilities will be averse to lending--charge a high interest rate premium on loans--to firms seen as having a high degree of undifferentiated idiosyncratic risk. Improvements in data collection and analysis that allow firms to differentiate will cause banks to fear undifferentiated firm-level idiosyncratic risk less, and charge lower interest rate premiums for such lending. Other things being equal, firms will smooth production more, and smooth cash-borrowing requirements less, seeking to squeeze out more productive efficiencies by taking on more financial risk. To the extent that improvements in data collection and analysis reduce banks' fixed costs of monitoring loans, other things being equal banks will do more to diversify away firm-level idiosyncratic risk.

When a bank goes bankrupt and defaults on its debt, it may be because it has had bad luck, it may be because it was badly managed, or it may be because it suffered from moral hazard--took account of the fact that in the lower tail the losses are eaten not by the banks' shareholders but by those who hold or guarantee its liabilities. Improvements in data collection and analysis by those to whom banks owe their liabilities will allow them to better classify banks, and so the cost to banks of portfolios with bank-level idiosyncratic risk will fall. Other things being equal, banks will be willing to take on more bank-level idiosyncratic risk.

Of course this function that Christina Wang identifies is the primary job--one of the primary jobs--of financial markets: to diversify away idiosyncratic risk, as was ably explicated by that notable predecessor of Lintner and Markowitz, William Shakespeare. As Shakespeare writes, Antonio, the Merchant of Venice, does not fear that the lower tail of his portfolio return distribution extends far enough down to the state in which his heart is cut out with a knife. Antonio he has a properly-diversified portfolio. The banker lending him the money uses the highest information technology of that day: wandering down to Venice's Grand Canal, loitering on the High Bridge, and gossiping. The banker concludes that Antonio has:

an argosy bound to Tripolis, another to the Indies; I understand moreover, upon the Rialto, he hath a third at Mexico, a fourth for England, and other ventures...

Here the analogy breaks down. Negative transitory systematic news does indeed provoke a crisis in Antonio's affairs, but he is rescued not by a competent, technocratic lender of last resort but by his bride disguised as a teenage judge.

Christina Wang hopes that starting sometime in the mid-1980s we took a jump toward the ideal financial world in which one of CAPM's cousins holds, in which idiosyncratic risk is not priced because it is properly diversified away, and in which as a result the real economy can grab for all the production-smoothing efficiency benefits without worrying about firm- or bank-level costs of default or illiquidity. This shift could drive a reduction in real-side volatility coupled with an increase or no change in financial-side volatility.

She has a nice theoretical costly-state-verification model of the effects of improved data collection and analysis technologies. She has a very interesting theoretical Dixit-Stiglitz-based three-period model of the joint determination of real and financial volatility. The key insight is a very good one: that production-smoothing has not just manufacturing-side and labor-side efficiency benefits but financial-side efficiency costs: only if banks are confident in their ability to monitor firms and large depositors confident in their ability to monitor banks will firms be able to easily and cheaply borrow the money they need in recession to enable a production-smoothing corporate strategy. The fact that times of recession are times when a firm's free cash is likely to be uniquely valuable and not to be best invested in building up inventories is a potentially powerful explanation of why we have, historically, seen the reverse of production-smoothing in the American economy. She has interesting empirical results that suggest that banks and firms have reacted to a likely information-driven fall in the cost of idiosyncratic financial risk to take on more of it. The theory is sound and convincing. The micro empirics are interesting and suggestive.

But how much can this channel add up to on the macro level? How, exactly, does ICT help bankers? Working for the original J.P. Morgan, Charlie Coster was on the boards of 88 railroads at the turn of the last century and died of overwork--Morgan is reputed to have recruited Coster's successor while they were together carrying Coster's coffin to its grave. What would today's ICT have done to increase Coster's contribution to Morgan's bottom line, exactly?

And how much of the Great Moderation in real-side economic volatility can this channel account for? Recall the size of the Great Moderation: a 40% fall in the standard deviation of the cyclical component of GDP, more or less the same however you choose to measure it. A fall in spite of the fact that technology and cost shocks have in all likelihood been quantitatively greater in the past ten years than in any other post-WWII decade save possibly the 1970s.

As Christina Wang says, her paper as written can't do the job. It can only do about a third of the job--although Doug Elmendorf said half last hour. The model as extended quite possibly could.

In this literature, the game that is being hunted is the positive correlation between production and inventory investment that we saw in the past. In a standard production-smoothing model inventory investment should be relatively high when production is relatively low, and sales are very low. Instead--back before 1985--inventory investment was high when production was high. This shift could be possibly traced to Christina Wang's mechanisms. But it can account, in my back-of-the-envelope guess, for not a 40% but a 15% decline in the standard deviation of the cyclical component, whatever that is.

The big game for this model--as Chistina Wang says in her conclusion--will, I think, come from applications of models like this to the household sector. It's not just firms that have benefitted from the application of information technology to credit screening. I have gotten three offers of VISA cards and two offers of what were described as "guaranteed low interest" home-equity loans so far this week. Plus the people behind the counter at my most local Starbucks have started asking me if I'm interested in a no-annual-fee Starbucks VISA that will come with $25 of free caffeinated drinks. I don't know whether they are doing this to everybody or whether there is something special in my file. The smoothing-out of household durables purchases will, I think, be an important part of the Great Moderation when we finally nail it down. And I think that's where the high returns from Christina Wang's model will come.

Last, the smoothing out of residential construction--if it indeed stays smoothed-out--may well turn out to be the heart of the matter. One branch of the conventional wisdom is that the smoothing-out of residential construction is a result of good luck that is about to end: that America's banks have been offered too much rice wine by the People's Bank of China, and have responded by lending like drunken bankers: $600,000 zero-down floating-rate loans to single-earner middle-class families buying three-bedroom houses in Vallejo, CA: and we will be sorry.

Christina Wang's paper suggests a second possible explanation. That recent residential investment financed by so-called "non standard" mortgage loans is a result at least in part not of the inebriation of the banking sector but of the ability to more finely calculate risk and return than was possible in the days when your mortgage had to be 30-year-fixed, 20% down, with amortization plus real estate taxes amounting to no more than 33% of last year's household income. That was an inadequate screen. What, really, are the current screens? How good are they? The application of models like this to residential financing may be the real big game here.